System and method for analyzing metabolomic data

ABSTRACT

The present invention generates a visual display of metabolomic data compiled by a database and associated processor. More particularly, the present invention provides a database for automatically receiving a three-dimensional spectrometry data set for a group of samples. The present invention also provides a processor device for manipulating the data sets to produce plots that are directly comparable to a plurality of characteristic plots corresponding to a plurality of selected metabolites. Furthermore, the processor device may generate a visual display indicating the presence of the selected metabolites across the group of samples. Thus, the present invention enables a user to analyze a series of complex data sets in a visual display that may indicate the presence of the selected metabolites across the group of samples. Furthermore, the visual display generated by embodiments of the present invention also expedites the subjective analysis of the spectrometry data sets.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a divisional of U.S. application Ser. No.12/245,862, filed Oct. 6, 2008, which is a continuation-in-part of U.S.patent application Ser. No. 11/462,838, filed Aug. 7, 2006, which claimsthe benefit of U.S. Provisional Application No. 60/706,459, filed Aug.8, 2005, all of which are incorporated by reference herein in theirentirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to the field of metabolomics, which is thestudy of small molecules produced by an organism's metabolic processes.More particularly, the embodiments of the present invention are adaptedto compile and compare metabolomic data received from a spectrometrydevice across a plurality of samples. Furthermore, embodiments of thepresent invention may also provide for the display of a visualindication of the presence of selected metabolites in each of theplurality of samples such that metabolomic data may be subjectivelyanalyzed by a user across the plurality of samples.

2. Description of Related Art

Metabolomics is the study of the small molecules, or metabolites,contained in a cell, tissue or organ (including fluids) and involved inprimary and intermediary metabolism. The term “metabolome” refers to thecollection of metabolites present in an organism. The human metabolomeencompasses native small molecules (natively biosynthesizeable,non-polymeric compounds) that are participants in general metabolicreactions and that are required for the maintenance, growth and normalfunction of a cell. Thus, metabolomics is a direct observation of thestatus of cellular physiology, and may thus be predictive of disease ina given organism. Subtle biochemical changes (including the presence ofselected metabolites) are inherent in a given disease. Therefore, theaccurate mapping of these changes to known pathways may allowresearchers to build a biochemical hypothesis for a disease. Based onthis hypothesis, the enzymes and proteins critical to the disease can beuncovered such that disease targets may be identified for treatment withtargeted pharmaceutical compounds or other therapy.

Molecular biology techniques for uncovering the biochemical processesunderlying disease have been centered on the genome, which consists ofthe genes that make up DNA, which is transcribed into RNA and thentranslated to proteins, which then make up the small molecules of thehuman metabolome. While genomics (study of the DNA-level biochemistry),transcript profiling (study of the RNA-level biochemistry), andproteomics (study of the protein-level biochemistry) are useful foridentification of disease pathways, these methods are complicated by thefact that there exist over 25,000 genes, 100,000 to 200,000 RNAtranscripts and up to 1,000,000 proteins in human cells. However, it isestimated that there may be as few as 2,500 small molecules in the humanmetabolome.

Thus, metabolomic technology provides a significant leap beyondgenomics, transcript profiling, and/or proteomics. With metabolomics,metabolites, and their role in the metabolism may be readily identified.In this context, the identification of disease targets may be expeditedwith greater accuracy relative to other known methods. The collection ofmetabolomic data for use in identifying disease pathways is generallyknown in the art, as described generally, for example, in U.S. Pat. Nos.7,005,255 and 7,329,489 to Metabolon, Inc., each entitled Methods forDrug Discovery, Disease Treatment, and Diagnosis Using Metabolomics.Additional uses for metabolomics data are described therein and include,for example, determining response to a therapeutic agent (i.e., drug) orother xenobiotics, monitoring drug response, determining drug safety,and drug discovery. However, the collection and sorting of metabolomicdata taken from a variety of samples (e.g., from a patient population)consumes large amounts of time and computational power. For example,according to some known metabolomic techniques, spectrometry data forcertain samples is collected and plotted in three dimensions and storedin an individual file corresponding to each sample. This data is thenindividually compared to data corresponding to a plurality of knownmetabolites in order to identify known metabolites that may be diseasetargets. The data may also be used for identification of toxic agentsand/or drug metabolites. Furthermore such data may also be used tomonitor the effects of xenobiotics. However, conventional “file-based”methods (referring to the data file generated for each sample) requirethe use of large amounts of computing power and memory assets to handlethe screening of large numbers of known metabolites. Furthermore,“file-based” data handling does not lend itself to the compilation ofsample population data across a number of samples because, according toknown metabolomic data handling techniques, each sample is analyzedindependently, without taking into account subtle changes in metabolitecomposition that may be more readily detectable across a samplepopulation. Furthermore, existing “file-based” method have otherlimitations including: limited security and audit ability; poor data setconsistency across multiple file copies; and individual files do notsupport multiple indices (example day collected, sample ID, control vs.treated, drug dose, etc) such that all files must be scanned when only asubset is desired.

These limitations in current metabolomic data analysis techniques maylead to the discarding of potentially relevant and/or valuablemetabolomic data that may be used to identify and classify particularmetabolites as disease targets. Specifically, spectrometry datacorresponding to a number of samples (such as tissue samples fromindividual human subjects) generally results in a large data filecorresponding to each sample, wherein each data file must then besubjected to a screening process using a library of known metabolites.However, conventional systems do not readily allow for the consolidationof spectrometry data from a number of samples for the subjectiveevaluation of the data generated by the spectrometry processes. Thus,while a single file corresponding to an individual sample may beinconclusive, such data may be more telling if viewed subjectively in asuccinct format with respect to other samples within a samplepopulation.

Therefore, there exists a need for an improved system to solve thetechnical problems outlined above that are associated with conventionalmetabolomic data analysis systems. More particularly, there exists aneed for a system capable of automatically receiving spectrometry datawithout the need to generate a separate data file for each sample. Therealso exists a need for a system capable of converting three-dimensionaldata sets into a corresponding two-dimensional data set and associatedplot, which may then be compared to a plurality of characteristic plotscorresponding to selected metabolites. In addition, there exists a needfor a system for allowing a user to subjectively evaluate thespectrometry data across a plurality of samples to identify selectedmetabolites, for allowing the user to verify or otherwise determine theconfidence in the identification of the selected metabolites, forallowing the user to examine the data associated with the identificationof the selected metabolites, and for allowing the user to determineadditional information related to the identified selected metabolites.

BRIEF SUMMARY OF THE INVENTION

The needs outlined above are met by the present invention which, invarious embodiments, provides systems and associated methods thatovercome many of the technical problems discussed above, as well othertechnical problems, with regard to the automated analysis (i.e.,compilation, translation, and concise display) of metabolomic data byconventional spectrometry data analysis systems.

More particularly, one embodiment provides a method of analyzingmetabolomics data, and a computer program product embodying such amethod, wherein a three-dimensional data set of metabolomics datacorresponding to each of a plurality of samples, is automaticallyreceived in a database from each of a first data source and a seconddata source. For the metabolomics data from each of the first and seconddata sources, the three-dimensional data set is converted into at leastone corresponding two-dimensional data set for each of the plurality ofsamples, and the at least one two-dimensional data set for each of theplurality of samples is plotted. The at least one plottedtwo-dimensional data set for each of the plurality of samples iscompared to a plurality of characteristic plots corresponding to aplurality of selected metabolites so as to screen the plurality ofsamples by associating at least one of the plurality of selectedmetabolites with the at least one plotted two-dimensional data set foreach of the plurality of samples. For the screened plurality of samplesdetermined to have the at least one of the plurality of selectedmetabolites associated therewith, the at least one plottedtwo-dimensional data set for each of the screened plurality of samplesis compared, across the screened plurality of samples, so as todetermine a trend of the associated at least one of the plurality ofselected metabolites, across the screened plurality of samples. A visualindication of the comparison of the at least one plotted two-dimensionaldata set for each of the screened plurality of samples, across aplurality of the screened plurality of samples, is then displayed tovisually indicate the screened plurality of samples determined to havethe at least one of the plurality of selected metabolites associatedtherewith, and the trend corresponding thereto, across the plurality ofthe screened plurality of samples, for the metabolomics data from eachof the first and second data sources. The first and second data sourcesare then compared with respect to the at least one of the plurality ofselected metabolites determined to be included in the metabolomics dataand the trend across the screened plurality of samples associatedtherewith so as to determine an identity confidence measure associatedwith the at least one of the plurality of selected metabolites.

Another aspect of the present invention provides a system for analyzingmetabolomics data, comprising a database in communication with each of afirst data source and a second data source, wherein the database isconfigured to automatically receive a three-dimensional data set ofmetabolomics data corresponding to each of a plurality of samples, fromeach of the first and second data sources. A processor device is incommunication with said database, wherein said processor device isconfigured, for the metabolomics data from each of the first and seconddata sources, to convert the three-dimensional data set into at leastone corresponding two-dimensional data set for each of the plurality ofsamples; plot the at least one two-dimensional data set for each of theplurality of samples; compare the at least one plotted two-dimensionaldata set for each of the plurality of samples to a plurality ofcharacteristic plots corresponding to a plurality of selectedmetabolites so as to screen the plurality of samples by associating atleast one of the plurality of selected metabolites with the at least oneplotted two-dimensional data set for each of the plurality of samples;and compare, for the screened plurality of samples determined to havethe at least one of the plurality of selected metabolites associatedtherewith, the at least one plotted two-dimensional data set for each ofthe screened plurality of samples, across the screened plurality ofsamples, so as to determine a trend of the associated at least one ofthe plurality of selected metabolites, across the screened plurality ofsamples. A user interface is in communication with said database andsaid processor device, wherein the user interface is configured todisplay a visual indication of the comparison of the at least oneplotted two-dimensional data set for each of the screened plurality ofsamples, across a plurality of the screened plurality of samples, tovisually indicate the screened plurality of samples determined to havethe at least one of the plurality of selected metabolites associatedtherewith, and the trend corresponding thereto, across the plurality ofthe screened plurality of samples, for the metabolomics data from eachof the first and second data sources. The displayed visual indication isfurther configured to facilitate determination of an identity confidencemeasure associated with the at least one of the plurality of selectedmetabolites from a comparison of the first and second data sources withrespect to the at least one of the plurality of selected metabolitesdetermined to be included in the metabolomics data and the trend acrossthe screened plurality of samples associated therewith.

Yet another aspect of the present invention provides a method foranalyzing metabolomics data, and a computer program product embodyingsuch a method, wherein a three-dimensional data set corresponding toeach of a plurality of samples is automatically received in a database,wherein each of the three-dimensional data sets has at least one of anexperimental factor, a sample factor, and a process factor associatedtherewith. The three-dimensional data set is converted into at least onecorresponding two-dimensional data set for each of the plurality ofsamples, such that the respective at, least one of an experimentalfactor, a sample factor, and a process factor remains associated withthe corresponding two-dimensional data set. The at least onetwo-dimensional data set is plotted for each of the plurality ofsamples. The at least one plotted two-dimensional data set for each ofthe plurality of samples is compared to a plurality of characteristicplots corresponding to a plurality of selected metabolites so as toscreen the plurality of samples by associating at least one of theplurality of selected metabolites with the at least one plottedtwo-dimensional data set for each of the plurality of samples. For thescreened plurality of samples determined to have the at least one of theplurality of selected metabolites associated therewith, the at least oneplotted two-dimensional data set for each of the screened plurality ofsamples is compared, across the screened plurality of samples, so as todetermine a trend of the associated at least one of the plurality ofselected metabolites, across the screened plurality of samples. A visualindication of the comparison of the at least one plotted two-dimensionaldata set for each of the screened plurality of samples is compared,across a plurality of the screened plurality of samples, to visuallyindicate the screened plurality of samples determined to have the atleast one of the plurality of selected metabolites associated therewith,and the trend corresponding thereto, across the plurality of thescreened plurality of samples. The comparison of the at least oneplotted two-dimensional data set for each of the screened plurality ofsamples is then sorted, across a plurality of the screened plurality ofsamples, by the at least one of an experimental factor, a sample factor,and a process factor associated with the corresponding two-dimensionaldata set, so as to visually indicate the screened plurality of samplesdetermined to have the at least one of the plurality of selectedmetabolites associated therewith, and the trend corresponding thereto,across the plurality of the screened plurality of samples, in relationto the at least one of an experimental factor, a sample factor, and aprocess factor.

Still another aspect of the present invention provides a system foranalyzing metabolomics data, comprising a database configured toautomatically receive a three-dimensional data set of metabolomics datacorresponding to each of a plurality of samples, wherein each of thethree-dimensional data sets has at least one of an experimental factor,a sample factor, and a process factor associated therewith. A processordevice is in communication with said database, and is configured toconvert the three-dimensional data set into at least one correspondingtwo-dimensional data set for each of the plurality of samples, such thatthe respective at least one of an experimental factor, a sample factor,and a process factor remains associated with the correspondingtwo-dimensional data set; plot the at least one two-dimensional data setfor each of the plurality of samples; compare the at least one plottedtwo-dimensional data set for each of the plurality of samples to aplurality of characteristic plots corresponding to a plurality ofselected metabolites so as to screen the plurality of samples byassociating at least one of the plurality of selected metabolites withthe at least one plotted two-dimensional data set for each of theplurality of samples; and compare, for the screened plurality of samplesdetermined to have the at least one of the plurality of selectedmetabolites associated therewith, the at least one plottedtwo-dimensional data set for each of the screened plurality of samples,across the screened plurality of samples, so as to determine a trend ofthe associated at least one of the plurality of selected metabolites,across the screened plurality of samples. A user interface is incommunication with said database and said processor device, wherein theuser interface is configured to display a visual indication of thecomparison of the at least one plotted two-dimensional data set for eachof the screened plurality of samples, across a plurality of the screenedplurality of samples, to visually indicate the screened plurality ofsamples determined to have the at least one of the plurality of selectedmetabolites associated therewith, and the trend corresponding thereto,across the plurality of the screened plurality of samples. The processordevice is further configured to cooperate with the user interface tosort the comparison of the at least one plotted two-dimensional data setfor each of the screened plurality of samples, across a plurality of thescreened plurality of samples, by the at least one of an experimentalfactor, a sample factor, and a process factor associated with thecorresponding two-dimensional data set, so as to visually indicate thescreened plurality of samples determined to have the at least one of theplurality of selected metabolites associated therewith, and the trendcorresponding thereto, across the plurality of the screened plurality ofsamples, in relation to the at least one of an experimental factor, asample factor, and a process factor.

Thus the systems, methods, and computer program products for analyzingmetabolomics data across a plurality of samples, as disclosed inconjunction with the embodiments of the present invention, provide manyadvantages that may include, but are not limited to: automaticallycompiling and indexing complex three-dimensional spectrometry data setsfor a plurality of samples so as to be capable of generating a samplepopulation data set; converting the complex three dimensional data setsinto two dimensional data sets and corresponding data plots that aremore easily comparable to a library of data plots corresponding to aplurality of selected metabolites of interest; providing a graphicalrepresentation of the spectrometry data analyses used to identifymetabolites of interest; and providing a graphical representation of thecompiled data across a population of samples such that the user of thesystem of the present invention may subjectively evaluate thespectrometry data and evaluate only those samples exhibiting a variancethat may be indicative of the absence and/or presence of a selectedmetabolite, without the need for opening a plurality of individual datafiles corresponding to each particular sample under examination. Indoing so, embodiments of the present invention, for example,advantageously allow a user to subjectively evaluate the spectrometrydata across a plurality of samples to identify selected metabolites,allow the user to verify or otherwise determine the confidence in theidentification of the selected metabolites, allow the user to examinethe data associated with the identification of the selected metabolites,and allow the user to determine additional information related to theidentified selected metabolites.

These advantages and others that will be evident to those skilled in theart are provided in the system, method, and computer program productembodiments of the present invention. Importantly, all of theseadvantages allow the system to display metabolomic analysis results to auser in a compact format that spans a “fourth dimension” across thepopulation of samples. Because, analytical results data spanning acrossthe sample population is made more readily evident to the user in agraphical format, along with the identification of metabolites ofinterest as potential disease targets, the user is better able todetermine the presence and/or absence of selected metabolites and/orchemical components within or otherwise associated with the samples.Furthermore, because additional sample population data is provided, theembodiments of the present invention are less likely to discountpotentially valuable spectrometry results that may be discounted whenviewed independently from the sample population.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 illustrates a system according to one embodiment of the presentinvention including a database, including a memory device and userinterface, in communication with a spectrometry device;

FIG. 2 a is an illustration of a three-dimensional plot of spectrometrydata typically associated with one exemplary sample;

FIG. 2 b is an illustration of a two-dimensional plot that may begenerated by some embodiments of the system of the present inventionthat may be comparable to a plurality of characteristic plotscorresponding to a plurality of selected metabolites;

FIG. 2 c is an illustration of a plot that may be generated by someembodiments of the system of the present invention including a visualindication of the presence of the selected metabolites in each of aplurality of samples;

FIG. 3 is an illustration of the operation flow of the systems, methods,and computer program products according to one exemplary embodiment;

FIG. 4 is an illustration of a verification/confirmation processregarding the presence of selected metabolites in each of a plurality ofsamples, according to one embodiment of the present invention, involvingpost-analysis data obtained from various analytical devices, channels ofa single analytical device, or combinations thereof;

FIG. 5 is an illustration of a verification/confirmation processregarding the presence of selected metabolites in each of a plurality ofsamples, according to one embodiment of the present invention, involvinga direct comparison of post-analysis data obtained from differentanalytical devices, or different channels of a single analytical device,or combinations thereof;

FIG. 6 is an illustration of a verification/confirmation processregarding the presence of selected metabolites in each of a plurality ofsamples, according to one embodiment of the present invention, whichinvolves sorting post analysis data with respect to at least one of anexperimental factor, a sample factor, and a process factor, anddesignating each such factor with a different indicia such as color;

FIG. 7A is an illustration of a verification/confirmation processregarding the presence of selected metabolites in each of a plurality ofsamples, according to one embodiment of the present invention, whichinvolves associating at least one related information source with eachof the screened plurality of samples determined to have the at least oneof the plurality of selected metabolites associated therewith;

FIGS. 7B and 7C are illustrations of a verification/confirmation processregarding the presence of selected metabolites in each of a plurality ofsamples, according to one embodiment of the present invention, whichinvolves associating one of the screened plurality of samples determinedto have the at least one of the plurality of selected metabolitesassociated therewith, with at least one related information sourcecomprising originally-collected (raw) data;

FIG. 8 is an illustration of a verification/confirmation processregarding the presence of selected metabolites in each of a plurality ofsamples, according to one embodiment of the present invention, whichinvolves relating metabolites and related chemicals and/or biochemicalsaccording to a particular common pathway;

FIG. 9 is an illustration of a verification/confirmation processregarding the presence of selected metabolites in each of a plurality ofsamples, according to one embodiment of the present invention, whichinvolves relating a particular selected metabolite identified as beingpresent in a particular sample or sample population, to completedstudies where the same selected metabolite may also have been identifiedas being present in the analyzed sample or sample population; and

FIG. 10 is an illustration of a verification/confirmation processregarding the presence of selected metabolites in each of a plurality ofsamples, according to one embodiment of the present invention, whichinvolves varying an axis parameter of a comparison across a plurality ofthe screened plurality of samples, so as to visually indicate an effectof a particular sample-related factor across the plurality of thescreened plurality of samples.

DETAILED DESCRIPTION OF THE INVENTION

The present inventions now will be described more fully hereinafter withreference to the accompanying drawings, in which some, but not allembodiments of the invention are shown. Indeed, these inventions may beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will satisfy applicable legalrequirements. Like numbers refer to like elements throughout.

The various aspects of the present invention mentioned above, as well asmany other aspects of the invention, are described in greater detailbelow. The systems, methods, and computer program products ofembodiments of the present invention are exemplarily disclosed inconjunction with an appropriate analytical device which may, in someinstances, comprise a separator portion (i.e., a chromatograph) and adetector portion (i.e., a spectrometer). It must be understood that thisis only one example of the implementation of embodiments of the presentinvention. Particularly, the systems, methods, and computer programproducts of embodiments of the present invention can be adapted to anynumber of processes that are used to generate complex sets of dataacross a plurality of samples, whether biological, chemical, orbiochemical, in nature. For example, embodiments of the presentinvention may be used with a variety of different analytical devices andprocesses including, but not limited to: analytical devices including aseparator portion comprising one of a liquid chromatograph (LC) and agas chromatograph (GC); a cooperating detector portion comprising one ofa nuclear magnetic resonance imaging (NMR) device; a mass spectrometer(MS); and an electrochemical array (EC); and/or combinations thereof. Inthis regard, one skilled in the art will appreciate that the embodimentsand aspects of the present invention as disclosed herein are not limitedto metabolomics analysis. For example, the embodiments and aspects ofthe present invention as disclosed herein can be can be implemented inother applications where there is a need to characterize the smallmolecules present within a sample, regardless of the origin of thesample. For instance, the embodiments and aspects of the presentinvention as disclosed herein can also be implemented in a bioprocessoptimization procedure where the goal is to grow cells to produce drugsor additives, in a drug metabolite profiling procedure where the goal isto identify all metabolites that are the result of biotranformations ofan administered xenobiotic. As will be appreciated by one skilled in theart, these exemplary applications may be very different from ametabolomics analysis, where the goal is only to examine endogenousmetabolites. Some other non-limiting examples of other applicationscould include a quality assurance procedure for consumer productmanufacturing where the goal may be to objectively ensure that desiredproduct characteristics are met, in procedures where a large number ofsample components can give rise to a particular attribute, such as tasteor flavor (e.g., cheese, wine or beer), or scent/smell (e.g.,fragrances). One common theme thus exhibited by the embodiments andaspects of the present invention as disclosed herein is that the smallmolecules in the sample can be determined using the various system,method and computer program product embodiments disclosed herein.

FIG. 1 illustrates an example of a system according to one embodiment ofthe present invention wherein the system is in communication with ananalytical device 110, such as a combination chromatograph/massspectrometer. One skilled in the art will appreciate, however, that theconfigurations of an analytical device 110 presented herein are forexemplary purposes only, and are not intended to be limiting withrespect to the scope of suitable and appropriate analytical devices thatmay also be applied under the principles disclosed herein As shown, asample (whether biological, chemical, or biochemical, in nature) 100 maybe introduced into the separator portion of the analytical device 110and analyzed using appropriate techniques, as applied through thedetector portion, that will be appreciated by those skilled in the art.For example, the components of a particular sample 100 may pass througha column associated with the separator portion, at different rates andexhibit different spectral responses via the detector portion based upontheir specific characteristics. As will be appreciated by one skilled inthe art, the analytical device 110 may generate a three-dimensional setof spectrometry data corresponding to each sample 100, wherein the dataincluded in the three-dimensional data set generally indicates thecomposition of the sample 100. However, such data must first beappropriately analyzed in order to determine the sample composition.

An example of such a three-dimensional set of spectrometry data is showngenerally in FIG. 2 a, and may be plotted on a three-axis plot,including axes for response intensity 220, component mass 210, and time230 (particularly, in this example, the time a particular componentspends in the column of the separator portion of the analytical device110). The location of data points relative to the component mass axis210 may be indicative, for example, of the number of individualcomponent molecules within the sample 100 and the relative mass valuesfor such components. According to other embodiments of the system of thepresent invention, alternate analytical devices may be used to generatea three-dimensional set of analytical data corresponding to the sample100. For example, the analytical device may include, but is not limitedto: various combinations of a separator portion comprising one of aliquid chromatograph (LC) and a gas chromatograph (GC); and acooperating detector portion comprising one of a nuclear magneticresonance imaging (NMR) device; a mass spectrometer (MS); and anelectrochemical array (EC). One skilled in the art will appreciate thatsuch complex three-dimensional data sets may be generated by otherappropriate analytical devices that may be in communication withcomponents of the system embodiments of the present invention asdescribed in further detail below.

A plurality of samples 100 may be taken individually from a well plate120 and/or from other types of sample containers and introducedindividually into the analytical device 110 for analysis and generationof the corresponding three-dimensional data set (see, e.g., FIG. 2 a).For example, individual samples 100 may be transferred from a well plate120 to the analytical device 110 via pipette, syringe, microfluidicpassageways defined by a test array, and/or other systems fortransferring samples in a laboratory environment. The samples mayinclude, but are not limited to: blood samples, urine samples, cellcultures, saliva samples, and/or other types of biological, chemical,and/or biochemical samples in which the metabolites and/or chemicalcomponents of interest may be present.

As shown in FIG. 1, the system embodiments of the present invention maycomprise a database (e.g., a relational database) stored, for example,in a memory device 140, wherein the database is in communication with aprocessor device 130 (e.g., a computer device), wherein the database(memory device 140) and/or the processor device 130 may be configured tobe in communication with the analytical device 110 for automaticallyreceiving a three-dimensional data set, corresponding to each of theplurality of samples 100, therefrom. The processor device 130 may be incommunication with the analytical device 110 via wire (RS-232, and/orother types of wire connection) and/or wireless (such as, for example,RF, IR, or other wireless communication) techniques such that thedatabase associated with the memory device 140/processor device 130(and/or in communication therewith) may receive the data set from theanalytical device 110 so as to be stored thereby Furthermore, theanalytical device 110 may be in communication with one or more processordevices 130 (and associated user interfaces 150) via a wired and/orwireless computer network including, but not limited to: the Internet,local area networks (LAN), wide area networks (WAN), or other networkingtypes and/or techniques that will be appreciated by one skilled in theart. The database may be structured using commercially-availablesoftware, such as, for example, Oracle, Sybase, DB2, or other databasesoftware. As shown in FIG. 1, the processor device 130 may be in furthercommunication with the memory device 140 (such as a hard drive, memorychip, flash memory, RAM module, ROM module, and/or other memory device140) for storing the database, including the three-dimensional data setsautomatically received from the analytical device 110. In addition, thememory device 140 may also be used to store other data received by thedatabase and/or otherwise manipulated by the processor device 130.

The processor device 130 may, in some embodiments, be capable ofconverting each of the three dimensional data sets (see FIG. 2 a)received by the database 140 into at least one correspondingtwo-dimensional data set (see FIG. 2 b), wherein the at least onetwo-dimensional data set comprises, for example, a two-dimensional“profile” of a particular sample 100 at a particular point 235 along thetime axis (wherein time is measured from a zero point, when the sample100 is injected and/or otherwise introduced into the analytical device110). For example, the processor device may produce a mass versusintensity profile of the sample at a given time point 235 (see FIG. 2 b,for example). The “x” axis (or time axis 230, for example) may furtherbe characterized as a retention index and/or a retention time. Thus, theprocessor device 130 may be further capable of parsing each of thethree-dimensional data sets into one or more individual two-dimensionalprofiles corresponding to particular points (235, for example) in timeso as to convert each three-dimensional data set (of FIG. 2 a, forexample) into at least one corresponding two-dimensional data set(having a profile shown, for example, in FIG. 2 b) that may further beplotted as an intensity response 220 versus mass 210.

Furthermore, according to some system embodiments, the processor device130 (in communication with the database) may be further configured tosystematically compare each of the at least one plotted two-dimensionaldata set (as shown, for example, in FIG. 2 b) to a plurality ofcharacteristic plots corresponding to a plurality of selectedmetabolites so as to screen the plurality of samples 100 for a presenceof at least one of the plurality of selected metabolites. As will beappreciated by one skilled in the art, selected known and/or known but“unnamed” metabolites may exhibit characteristic mass vs. intensityprofiles in association with a characteristic retention time withrespect to the separator portion of the analytical device 110.Specifically, in some embodiments, the processor device 130 may compriseand/or be in communication with the memory device 140 for storing theplurality of characteristic plots corresponding to the selectedmetabolites (i.e., in a “library” or repository of such characteristicplots). For example, one skilled in the art will appreciate thatmetabolites may be identified with varying degrees of certainty (e.g.,confidence) by comparing a two-dimensional plot of response intensity(sometimes expressed in μA (as shown in FIG. 2 b) resulting from a massspectrometry analysis with stored two-dimensional profiles correspondingto selected metabolites, for instance, through a “peak matching”procedure. Thus, embodiments of the present invention may be capable ofsystematically comparing each of the at least one plottedtwo-dimensional data sets (correspond, for example, to each sample 100)to a series of characteristic plots corresponding to selectedmetabolites, in order to facilitate an identification thereof. It shouldbe understood that the various system and method embodiments of thepresent invention may be capable of comparing each of the at least oneplotted two-dimensional data sets to a series of characteristic plotscorresponding to selected metabolites, wherein the characteristic plotsmay correspond to “known named” and/or “known, but unnamed”chemicals/compounds. For example, embodiments of the present inventionare capable of utilizing characteristic plots (stored in a memory device140, for example) that correspond both to metabolites having chemicalnames and/or those “known, but unnamed” metabolites for whichcharacteristic plots have been identified, but for which a chemical nameand/or classification has not yet been assigned. Further, for thescreened plurality of samples determined to have the at least one of theplurality of selected metabolites associated therewith, the processordevice 130 may be further configured to compare the at least one plottedtwo-dimensional data set for each of the screened plurality of samplesmay (i.e., with respect to each other), across the screened plurality ofsamples, so as to determine a trend or other relationship of theassociated at least one of the plurality of selected metabolites, acrossthe screened plurality of samples.

Furthermore, because the database of the present invention is capable ofstoring metabolomic data from a plurality samples 100, in a singlerelational database, sample 100 data (specifically profile datacorresponding to certain selected time points) can be readily determinedand/or compared, such that the certainty of or confidence in a “match”between the at least one plotted two-dimensional data set and one of thecharacteristic plots of selected metabolites (or other chemicals) may beused to infer the presence of a selected metabolite in a sample 100, ininstances when there may be a less-than-certain profile match. Thus, auser of system embodiments of the present invention may be able tosubjectively (or, in other instances, at least partially objectively)determine that a plotted two-dimensional data set that may, for example,fail to meet a minimum threshold for a “certain” identification, mayactually be indicative of the presence of a selected metabolite, given aprevalence of certain correlations among the corresponding plottedtwo-dimensional data sets across an overall population of samples 100.Embodiments of the present invention may thus utilize a visualindication of sufficiently “certain” matches (i.e., with sufficientconfidence) across a population of samples 100 (as shown in FIG. 2 c,for example) to infer that uncertain, but likely matches are, in fact,valid data points and indicative of a “match” that, in turn, indicatesthe presence of a selected metabolite and/or chemical component in thesample 100.

For example, the profile shown in FIG. 2 b, in the case of some samples100, may match a characteristic plot of a selected metabolite (such as aknown metabolite and/or a known, but unnamed metabolite, for example)with sufficient certainty (which may be shown as a data point within aselected time frame 260, 270, as shown in FIG. 2 c, wherein the datapoint may represent, for example, an intensity peak in the correspondingtwo-dimensional data set). However, due to variations in samples,potential contaminants within samples 100, and/or other experimentalfactors that will be appreciated by those skilled in the art, somethree-dimensional data sets generated by the analysis of some samples100 may result in corresponding two-dimensional mass versus responseintensity profiles at a given time that may not match a characteristicplot with such sufficient certainty. In existing “file-based” dataanalysis systems, each sample 100 is analyzed individually such that thepresence of certain metabolites or other chemicals may be unduly and/orprematurely discounted due to the fact that the two-dimensional data set(and corresponding profile plot (as shown in FIG. 2 b)) is not capableof being subjectively analyzed in light of an associated population ofsamples 100 (i.e., as a measure of consistency in the identification ofthe particular metabolite(s)).

Embodiments of the system of the present invention, may further comprisea user interface 150 in communication with said processor device130/memory device 140 for displaying a visual indication 160 (see also,FIG. 2 c, for example) of the presence of the selected metabolite(s) (asindicated by intensity peaks and certain metabolite “matches”) acrossthe plurality of samples 100 on a time axis 240 that is indicative ofthe time at which selected intensity peaks were determined from the datadetected by the analytical device 110. The user interface 150 may becapable of displaying to the user, for example, a display 160 of samplenumber 250 (indicating the identity of the sample 100) versus time 240,as shown generally in FIG. 2 c. More particularly, in some systemembodiments, the user interface 150, in communication with thedatabase/processor device 130, may be configured to display a visualindication of the comparison of the at least one plotted two-dimensionaldata set for each of the screened plurality of samples, across aplurality of the screened plurality of samples, to visually indicate thescreened plurality of samples determined to have the at least one of theplurality of selected metabolites associated therewith, and the trend orother relationship corresponding thereto, across the plurality of thescreened plurality of samples.

The user interface 150 may comprise, for example, a display device,personal computer, and/or other electronic device having a display forgraphical representation of data. For example, as shown in FIG. 2 c, agraphical plot of time 240 versus sample number 250 may be generated bythe database/processor device 130 and displayed via the user interface150 such that differences in the component makeup of each sample and/orthe differences between samples in an overall population of samples 100may be visually discernable by a user of the system embodiments of thepresent invention. In some embodiments, the user interface 150 and/orprocessor device 130 may be capable of generating time threshold markers260, 270 so that a user may specify visual boundaries on the display 160(FIG. 2 c) in order to readily identify outlying data associated withsome samples (for example, Sample #7 as shown in FIG. 2 c) that may failto exhibit intensity peaks falling within the selected time frame (asdefined by time thresholds 260, 270), and thus may not provide asufficient “match.” Thus, a user of the system of the present inventionmay utilize the multi-sample visual depiction of FIG. 2 c to discern aspecific time 265 about which intensity peaks were detected in themajority of samples 100 displayed on the display 160. The systemembodiments of the present invention may then call up or otherwiseaccess specific profile plots for certain metabolites (see FIG. 2 b, forexample) corresponding to the specific time 265, and then compareprofile plots to any of the two-dimensional plots of any of the samplesshown in FIG. 2 c, such that a user may then subjectively determine ifone of the profile plots (of mass 210 vs. intensity 220) indicates thatthe selected sample does indeed indicate the presence of thecorresponding selected metabolite (which may be indicated, for example,by a less-than-optimal intensity peak for a given characteristic mass).

Furthermore, as shown in FIG. 2 c, the system embodiments of the presentinvention may also allow a user to compile and display multiple timethresholds 260, 270 during which peak intensities were objectivelydetermined (by a user and/or system-defined threshold) for each of thesamples 250. That is, FIG. 2 c represents, for example, the location ofcertain intensity peaks, as they relate to the multiple time thresholds260, 270, across the plotted two-dimensional data sets for each of thesamples 250. As such, the analysis results are displayed to a user in acompact format that spans a “fourth dimension” across the population ofsamples. This displayed information may further add to evidence that maybe utilized by a user to make the subjective decision to accept a “lessthan certain” profile match as an indication of the presence of aselected metabolite. For example, as described above, and shown in FIG.2 c, no objective intensity peak for Sample #7 was detected by theanalytical device 110 at time t1, or was otherwise not evident in thetwo-dimensional data set for that sample. However, objective intensitypeaks were ostensibly detected in all other samples shown in thisexample. Thus, the processor device 130 may detect this aberration andautomatically display the two-dimensional profile information (see FIG.2 b) to a user, via the user interface, for Sample #7 at time t1, suchthat a subjective user decision may be made (i.e., match or no match)with respect to the analytical device 110 objective determination.Furthermore, the system embodiment of the present invention may alsoallow a user to further confirm the subjective determination of thepresence of an obscured and/or otherwise undetected peak at t1 (e.g.,for Sample #7) by displaying (in the time 240 versus sample 250 plotformat of FIG. 2 c) the presence of an objective intensity “peak” attime t2 for further comparison.

System embodiments of the present invention may thus allow a user toquickly discern which samples 100 (displayed on a time plot 160, via auser interface 150) may require further subjective scrutiny.Furthermore, using the time threshold markers 260, 270 in conjunctionwith the display 160 of the entire population of sample 100 results, theprocessor device 130 of the present invention may then automaticallyidentify and retrieve (from the memory device 140 and/or database 130)the series of at least one two-dimensional plots (see FIG. 2 b)corresponding to the samples 100 that may not exhibit a sufficient“match” or characteristic intensity peaks at the selected times 265 whencompared to the overall population of samples. The two-dimensional plotsretrieved from the database (i.e., corresponding to the samplesexhibiting the outlying points 280) may then be subjectively analyzed todetermine if the absence of a characteristic peak at the selected time265 is indicative of a lack and/or presence of selected metabolites thatmay differ from the chemical composition of the majority of the analyzedpopulation of samples 100.

Some system embodiments may thus generate a listing or other indicia ofone or more metabolites present in the samples, from the overallpopulation of samples 100, on the graphical display 150 as shown, forexample, in FIG. 2 c. The listing of identified metabolites may thusprovide, for example, a corresponding list of disease targets forpharmaceutical development or other therapy. In addition, in someembodiments, the processor device 130, in cooperation with the database,may also be used to map clinical data including, but not limited to:symptoms exhibited by patients from whom the samples 100 have beentaken; known disease states of the patients from whom the samples 100have been taken; patient physiological data; other supplemental patientdata (height, weight, age, etc.); and/or other clinical data. Thus,using the database of the present invention, other clinical/experimentalfactors may be correlated with the identified presence of selectedmetabolites in one or more samples 100 such that the database/processordevice 130 may be used to generate predictive tests for diseases and/ordisorders that may exhibit characteristic and measurable changes inmetabolomic data.

Embodiments of the present invention also provide a method for compilingand comparing (i.e., analyzing) metabolomics data across a plurality ofsamples 100, as shown generally in the flow diagram of FIG. 3. Step 310comprises automatically receiving a three-dimensional data set (such asthat shown graphically in FIG. 2 a) corresponding to each of theplurality of samples 100 in a database. As shown generally in FIG. 1,and as described above, the three-dimensional data set may comprisespectrometry data received by the database/processor device 130 whichmay be in communication (via wired and/or wireless connection) with aprocessor device 130 (such as a computer device) and an analyticaldevice 110 (i.e., a chromatograph/mass spectrometer) capable ofgenerating analytical data corresponding to a sample 100 containing aplurality of metabolites and other chemical components. In someembodiments, wherein the three-dimensional data set is generated by andreceived from a mass spectrometer, the axes defining thethree-dimensional data set may comprise, as shown generally in FIG. 2 a:component mass 210, response intensity 220, and time 230 (wherein timerefers to the time a particular component of the sample 100 is retainedin the column of the spectrometer 110). Furthermore, the automaticreceiving step 310 may be repeated for a number of samples 100 in adefined population of samples, wherein the resulting data isautomatically compiled by the processor device 130 of the presentinvention such that analytical data corresponding to each sample 100 isindexed and remains selectively retrievable from the database by theprocessor device 130 (and/or a computer device or user interface 150 incommunication therewith).

More specifically, in some method embodiments, the automatic receivingstep 310 may further comprise: compiling the three-dimensional dataassociated with a plurality of samples 100 in order to generate apopulation data set (that may be used to further determine, for example,statistical boundaries of the sample population); and indexing thethree-dimensional data according to the individual sample 100 such that,as statistical outliers (see Sample #7 at FIG. 2 c, for example) amongthe samples 100 are identified relative to the overall population ofsamples, the data corresponding to the particular “statistical outlier”samples 100 (i.e., Sample #7) may be retrieved from the database by theprocessor device 130 and displayed to a user (e.g., via a user interface150), for instance, as a two-dimensional plot, for a subjectivedetermination of whether one or more selected metabolites are present inthose samples 100. As described above, the system embodiments of thepresent invention may be used to define time thresholds (see elements260, 270 of FIG. 2 c) such that the database/processor device 130 mayretrieve data corresponding to samples 100 exhibiting a selectedvariance with respect to those time thresholds, from the samplepopulation.

Step 320 of the method embodiments of the present invention comprisesconverting each three-dimensional data set (shown generally in FIG. 2 a)into at least one corresponding two-dimensional data set (showngenerally in FIG. 2 b). Step 320 may comprise, for example, parsing thethree-dimensional data set along the time axis 220 such that eachcomponent of the sample 100 having a different retention time may beassociated with a corresponding two-dimensional data set plotted asresponse intensity 220 versus mass 210 (e.g., see FIG. 2 b). Methodembodiments of the present invention may also comprise Step 330 forplotting the at least one two-dimensional data set, as shown in FIG. 2b, for each of the plurality of samples. For example, in methodembodiments wherein spectrometry data is automatically received by thedatabase in step 310, step 330 may comprise plotting eachtwo-dimensional data set (corresponding to a sample 100 component havingcharacteristic retention time within a spectrometer column, for example)relative to mass 210 and response intensity 220 axes that may bedirectly comparable to characteristic plots of selected metabolites.Thus, method step 340 comprises comparing the at least one plottedtwo-dimensional data set (see FIG. 2 b, for example) to a plurality ofcharacteristic plots corresponding to a plurality of selectedmetabolites so as to screen the plurality of samples 100 by associatingat least one of the plurality of selected metabolites with the at leastone plotted two-dimensional data set for each of the plurality ofsamples.

One skilled in the art will appreciate that such two-dimensionalprofiles may be indicative of the presence and/or absence of aparticular metabolite and/or other chemical component within a sample100 as analyzed by the mass spectrometer. However, one skilled in theart will also appreciate that only a certain amount of samples mayproduce mass versus response intensity profiles that indicate a certainor definite match with a selected metabolite. Method embodiments of thepresent invention may thus also comprise step 330 for plotting the atleast one two-dimensional data set (corresponding to a sample 100component having a particular mass, for example), as shown in FIG. 2 b,for each of the plurality of samples, relative to time 230 and responseintensity 220 axes that may be directly comparable to characteristicplots of selected metabolites. Thus, method step 340 comprises comparingthe at least one plotted two-dimensional data set (see FIG. 2 b, forexample) to a plurality of characteristic plots corresponding to aplurality of selected metabolites so as to screen the plurality ofsamples 100 for a presence of at least one of the plurality of selectedmetabolites. As described herein with respect to the various systemembodiments of the present invention, it should be understood that themethod embodiments may also be capable of comparing each of the at leastone plotted two-dimensional data sets to a series of characteristicplots corresponding to selected metabolites that may include, but arenot limited to: “known and named metabolites,” and/or “known, butunnamed” metabolites. For example, various method embodiments describedherein are capable of utilizing characteristic plots that correspondboth to metabolites having chemical names and/or those “known, butunnamed” metabolites for which characteristic plots have beenidentified, but for which a chemical name and/or classification has notyet been assigned.

For the screened plurality of samples determined to have the at leastone of the plurality of selected metabolites associated therewith, theat least one plotted two-dimensional data set for each of the screenedplurality of samples may then be compared (i.e., with respect to eachother), across the screened plurality of samples, so as to determine atrend or other relationship of the associated at least one of theplurality of selected metabolites, across the screened plurality ofsamples.

As shown in FIG. 3, Step 350 comprises displaying a visual indication ofthe comparison of the at least one plotted two-dimensional data set foreach of the screened plurality of samples, across a plurality of thescreened plurality of samples, to visually indicate the screenedplurality of samples determined to have the at least one of theplurality of selected metabolites associated therewith, and any trendcorresponding thereto, across the plurality of the screened plurality ofsamples 100 (i.e., in a “fourth dimension” across the plurality ofsamples). According to some embodiments, this method step may furthercomprise: generating a graphical display (as shown, for example, in FIG.2 c) of time 240 (i.e., time of detection of an objective matchingintensity “peak”) versus sample number 250 to first facilitateidentification of samples 100 having a lack of characteristic peakswithin a selected time period (as defined by, for example, timethresholds 260, 270) when compared to the overall associated samplepopulation; and retrieving the at least one two-dimensional data setcorresponding to the samples 100 exhibiting a lack of characteristicpeak(s) in response to such identification such that a user may make asubjective determination of the presence or absence of a selectedmetabolite and/or chemical component. Finally, step 350, may, in someembodiments, further comprise providing a listing or other indicia ofmetabolites and/or chemical components that are present in the analyzedsamples 100.

Thus, the method embodiments of the present invention may allow for thesubjective determination of the presence and/or absence of a selectedmetabolite and/or chemical component within a sample 100 by linking datafrom an associated population of samples in a database such that, ascharacteristic data sets (i.e., as plotted in FIG. 2 b) are accumulatedfor each sample, the data sets may be linked and further utilized to mapcharacteristic data across the plurality of samples 100 (as plotted, forexample, in FIG. 2 c). The visual indication of FIG. 2 c may thus allowfor the ready identification of specific samples that may requirefurther subjective scrutiny. This may be especially apparent, forexample, in FIG. 2 c, when a particular sample does not exhibitcharacteristic peaks and/or profile matches at each time of interest265, but does conform to the overall population data in other respects.While previous data analysis methods may have discounted and/ordiscarded such samples 100, the method of embodiments of the presentinvention allows a user to highlight such samples for closer subjectivescrutiny, such that the aberrations may be either explained and/ordetermined to be indicative of a fundamental chemical difference of theparticular sample with respect to the associated population of samples.

Another aspect of the present invention is directed to the verificationor confirmation of the identification (or lack of identification) of theat least one selected metabolite determined to be commonly associatedwith the plurality of samples comprising the analyzed population ofsamples. For example, inconsistent analysis results may, in someinstances, be obtained from verification studies due to, for instance,variations between analytical devices, or even variations betweenchannels of a single analytical device. In another respect, particularanalytical devices, or even particular channels of a single analyticaldevice, may be known to have certain characteristics reflected in theanalytical data obtained therefrom. Accordingly, if various analyticaldevices have quantifiable or otherwise known characteristics/behaviors,the analysis of the sample population according to method embodimentsdisclosed herein, can be extended to encompass data acquired from morethan one analytical device, or more than one channel of a singleanalytical device, or combinations thereof (see, e.g., FIG. 4, elements410, 420, and 430 which represents sample data obtained, for example,from samples exposed to a separator portion of the analytical devicecomprising one of the positive channel of a liquid chromatograph, a gaschromatograph, and the negative channel of a liquid chromatograph,respectively), for verifying or otherwise confirming the analysisresults for the subject population of samples. In some instances, thisanalysis verification/confirmation principle may further be extended toinclude an analysis of data associated with differentretention/detection times for a single analytical device (see, e.g.,FIG. 4, elements 400 and 420 which represent sample data obtained, forexample, at different retention times 440, 450 in separator portioncomprising a gas chromatograph, possibly the same gas chromatograph), orsingle channel of a certain analytical device, as previously disclosed.

More particularly, such a method of analyzing metabolomics data, and acomputer program product embodying such a method, comprisesautomatically receiving a three-dimensional data set of metabolomicsdata, corresponding to each of a plurality of samples, in a databasefrom each of a first data source 110 (see, e.g., FIG. 1) and a seconddata source 115 (see, e.g., FIG. 1). Each data source may comprise, forexample, a different analytical device (i.e., various combinations of aseparator portion comprising one of a liquid chromatograph (LC) and agas chromatograph (GC); and a cooperating detector portion comprisingone of a nuclear magnetic resonance imaging (NMR) device; a massspectrometer (MS); and an electrochemical array (EC)), differentchannels of a separator portion of a single analytical device (i.e., apositive channel and a negative channel of a liquid chromatograph), orcombinations thereof. For the metabolomics data from each of the firstand second data sources 110, 115, the three-dimensional data set (see,e.g., FIG. 2 a) is converted into at least one correspondingtwo-dimensional data set (see, e.g., FIG. 2 b) for each of the pluralityof samples, and the at least one two-dimensional data set for each ofthe plurality of samples is plotted. The at least one plottedtwo-dimensional data set for each of the plurality of samples iscompared to a plurality of characteristic plots corresponding to aplurality of selected metabolites so as to screen the plurality ofsamples by associating at least one of the plurality of selectedmetabolites with the at least one plotted two-dimensional data set foreach of the plurality of samples. For the screened plurality of samplesdetermined to have the at least one of the plurality of selectedmetabolites associated therewith, the at least one plottedtwo-dimensional data set for each of the screened plurality of samplesis compared, across the screened plurality of samples, so as todetermine a trend of the associated at least one of the plurality ofselected metabolites, across the screened plurality of samples. A visualindication of the comparison of the at least one plotted two-dimensionaldata set for each of the screened plurality of samples, across aplurality of the screened plurality of samples, is then displayed (see,e.g., FIG. 2 c) to visually indicate the screened plurality of samplesdetermined to have the at least one of the plurality of selectedmetabolites associated therewith, and the trend corresponding thereto,across the plurality of the screened plurality of samples, for themetabolomics data from each of the first and second data sources. Thefirst and second data sources are then compared with respect to the atleast one of the plurality of selected metabolites determined to beincluded in the metabolomics data (see, .e.g., FIG. 4), and the trendacross the screened plurality of samples associated therewith (see,.e.g., FIG. 5), so as to determine an identity confidence measureassociated with the at least one of the plurality of selectedmetabolites.

Similarly, an associated apparatus/system embodying the disclosedanalysis verification/confirmation principle for analyzing metabolomicsdata, comprises a database in communication with each of the first datasource 110 and the second data source 115, as previously disclosed,wherein the database is configured to automatically receive athree-dimensional data set of metabolomics data corresponding to each ofa plurality of samples, from each of the first and second data sources110, 115. A processor device 130 is in communication with the database,wherein the processor device 130 is configured, for the metabolomicsdata from each of the first and second data sources 110, 115, to convertthe three-dimensional data set into at least one correspondingtwo-dimensional data set for each of the plurality of samples; plot theat least one two-dimensional data set for each of the plurality ofsamples; compare the at least one plotted two-dimensional data set foreach of the plurality of samples to a plurality of characteristic plotscorresponding to a plurality of selected metabolites so as to screen theplurality of samples by associating at least one of the plurality ofselected metabolites with the at least one plotted two-dimensional dataset for each of the plurality of samples; and compare, for the screenedplurality of samples determined to have the at least one of theplurality of selected metabolites associated therewith, the at least oneplotted two-dimensional data set for each of the screened plurality ofsamples, across the screened plurality of samples, so as to determine atrend of the associated at least one of the plurality of selectedmetabolites, across the screened plurality of samples. A user interface150 (see, e.g., FIG. 1) is in communication with the database and theprocessor device 130, wherein the user interface 150 is configured todisplay a visual indication of the comparison of the at least oneplotted two-dimensional data set for each of the screened plurality ofsamples, across a plurality of the screened plurality of samples, tovisually indicate the screened plurality of samples determined to havethe at least one of the plurality of selected metabolites associatedtherewith, and the trend corresponding thereto, across the plurality ofthe screened plurality of samples, for the metabolomics data from eachof the first and second data sources 110, 115. The displayed visualindication is further configured to facilitate determination of anidentity confidence measure associated with the at least one of theplurality of selected metabolites from a comparison of the first andsecond data sources 110, 115 with respect to the at least one of theplurality of selected metabolites determined to be included in themetabolomics data and the trend across the screened plurality of samplesassociated therewith. In some instances, as shown in FIG. 5 (comparingdata from the positive and negative channels 500, 510 of a liquidchromatograph), the comparison of the first and second data sources 110,115 with respect to the at least one of the plurality of selectedmetabolites determined to be included in the metabolomics data and thetrend across the screened plurality of samples associated therewith, maybe displayed together on a single plot, wherein this correlation of theresponses of various analytical devices, channels of a single analyticaldevice, or combinations thereof, facilitates an evaluation of theconfidence or certainty of the identification of the one or moreselected metabolites.

Yet another aspect of the present invention is directed to facilitatingaccess to information regarding the at least one of the plurality ofselected metabolites determined to be included in the metabolomics dataand the trend across the screened plurality of samples associatedtherewith, in part to further the confirmation of, or otherwise verify,the identification and determination thereof. For example, one method offurther facilitating the analysis of the data received from theanalytical device(s) interacting with the sample(s) is to associateappropriate experimental factors, sample factors, process factors, otherrelevant factors, or combinations thereof, with each three-dimensionaldata set received by the database and corresponding to a respective oneof the plurality of samples. By associating such relevant factors withthe data, upon the data being collected by the analytical device(s), thepost-analysis data maybe further examined with respect to the associatedfactors in order to determine, for example, if one or more of suchfactors may have affected the collected data, and thus possibly affectedthe results of the analysis itself (i.e., the identification of the oneor more selected metabolites).

More particularly, such a method of analyzing metabolomics data, and acomputer program product embodying such a method, comprisesautomatically receiving a three-dimensional data set corresponding toeach of a plurality of samples in a database, wherein each of thethree-dimensional data sets has at least one of an experimental factor,a sample factor, and a process factor associated therewith. Anexperimental factor may include, for example, the age or sex of thesubject from whom the sample was obtained, whether the subject wasdiseased, etc. A sample factor may include, for example, the conditionsunder which the sample was prepared, the chain of custody of the sample,etc. A process factor may include, for example, the run order of thesample through the analytical device, characteristics of the particularanalytical device, sample batch data, etc. One skilled in the art,however, will appreciate that the terms “experimental factor,” “samplefactor,” and “process factor” are only examples of the various factorsthat may appropriately be associated with the collected data, and whichmay be useful in further examining the post-analysis data. As such,these terms and the inclusion thereof in the specifications and theclaims of the present invention are not intended to be limiting withrespect to the scope of the “factors” that may be associated with thecollected data in this manner.

The three-dimensional data set (see, e.g., FIG. 2 a) is then convertedby the processor device 130 into at least one correspondingtwo-dimensional data set for each of the plurality of samples, such thatthe respective at least one of an experimental factor, a sample factor,and a process factor remains associated with the correspondingtwo-dimensional data set. The at least one two-dimensional data set isplotted for each of the plurality of samples (see, e.g., FIG. 2 b). Theat least one plotted two-dimensional data set for each of the pluralityof samples is then compared (e.g., by the processor device 130 inFIG. 1) to a plurality of characteristic plots corresponding to aplurality of selected metabolites so as to screen the plurality ofsamples by associating at least one of the plurality of selectedmetabolites with the at least one plotted two-dimensional data set foreach of the plurality of samples. For the screened plurality of samplesdetermined to have the at least one of the plurality of selectedmetabolites associated therewith, the at least one plottedtwo-dimensional data set for each of the screened plurality of samplesis compared, across the screened plurality of samples, so as todetermine a trend of the associated at least one of the plurality ofselected metabolites, across the screened plurality of samples. A visualindication of the comparison (e.g., as displayed on a user interface 150in FIG. 1) of the at least one plotted two-dimensional data set for eachof the screened plurality of samples is compared, across a plurality ofthe screened plurality of samples, to visually indicate the screenedplurality of samples determined to have the at least one of theplurality of selected metabolites associated therewith, and the trendcorresponding thereto, across the plurality of the screened plurality ofsamples (see, e.g., FIG. 2 c). The comparison of the at least oneplotted two-dimensional data set for each of the screened plurality ofsamples displayed on the user interface 130 is then sorted, across aplurality of the screened plurality of samples, by the at least one ofan experimental factor, a sample factor, and a process factor associatedwith the corresponding two-dimensional data set, so as to visuallyindicate the screened plurality of samples determined to have the atleast one of the plurality of selected metabolites associated therewith,and the trend corresponding thereto, across the plurality of thescreened plurality of samples, in relation to the at least one of anexperimental factor, a sample factor, and a process factor (see, e.g.,FIG. 6). In one instance, as shown in FIG. 6, each of the at least oneof an experimental factor, a sample factor, and a process factor may bedesignated with a different color (see, e.g., elements 600-640)according to which the comparison may be further sorted. The factors ofinterest may be selected, for example, a “drop-down” menu 650 associatedwith the display of the comparison. As such, upon further sorting of thecomparison, any effect of a particular factor of interest on thedisplayed post-analysis data may be visually discerned through, forinstance, the color associated with the particular factor and thelocation of relevant data with respect thereto (i.e., factor 630 in FIG.6 would appear to be relevant to the post-analysis data) as implementedthrough cooperation between the processor device 130 and the userinterface 150.

Similarly, an associated apparatus/system embodying the disclosedfurther verification/confirmation principle for analyzing metabolomicsdata, comprises a database configured to automatically receive athree-dimensional data set of metabolomics data corresponding to each ofa plurality of samples, wherein each of the three-dimensional data setshas at least one of an experimental factor, a sample factor, and aprocess factor associated therewith. A processor device 130 (see, e.g.,FIG. 1) is in communication with the database, wherein the processordevice 130 is configured to convert the three-dimensional data set intoat least one corresponding two-dimensional data set for each of theplurality of samples, such that the respective at least one of anexperimental factor, a sample factor, and a process factor remainsassociated with the corresponding two-dimensional data set; plot the atleast one two-dimensional data set for each of the plurality of samples;compare the at least one plotted two-dimensional data set for each ofthe plurality of samples to a plurality of characteristic plotscorresponding to a plurality of selected metabolites so as to screen theplurality of samples by associating at least one of the plurality ofselected metabolites with the at least one plotted two-dimensional dataset for each of the plurality of samples; and compare, for the screenedplurality of samples determined to have the at least one of theplurality of selected metabolites associated therewith, the at least oneplotted two-dimensional data set for each of the screened plurality ofsamples, across the screened plurality of samples, so as to determine atrend of the associated at least one of the plurality of selectedmetabolites, across the screened plurality of samples. A user interface150 (see, e.g., FIG. 1) is in communication with the database and theprocessor device 130, wherein the user interface 150 is configured todisplay a visual indication of the comparison of the at least oneplotted two-dimensional data set for each of the screened plurality ofsamples, across a plurality of the screened plurality of samples, tovisually indicate the screened plurality of samples determined to havethe at least one of the plurality of selected metabolites associatedtherewith, and the trend corresponding thereto, across the plurality ofthe screened plurality of samples. The processor device 130 is furtherconfigured to cooperate with the user interface 150 to sort thecomparison of the at least one plotted two-dimensional data set for eachof the screened plurality of samples, across a plurality of the screenedplurality of samples, by the at least one of an experimental factor, asample factor, and a process factor associated with the correspondingtwo-dimensional data set, so as to visually indicate the screenedplurality of samples determined to have the at least one of theplurality of selected metabolites associated therewith, and the trendcorresponding thereto, across the plurality of the screened plurality ofsamples, in relation to the at least one of an experimental factor, asample factor, and a process factor.

Still further aspects of the present invention are directed toadditional measures related to the disclosed furtherverification/confirmation principle for analyzing metabolomics data. Forexample, as shown in FIG. 7A, each of the screened plurality of samplesdetermined to have the at least one of the plurality of selectedmetabolites associated therewith, may be associated with at least oneinformation source related thereto, as implemented through cooperation,for example, between the processor device 130 and the database. Suchinformation sources may comprise, for example, the database as itrelates to the at least one of an experimental factor, a sample factor,and a process factor associated with the originally-collected data(i.e., an experimental factor information source, a sample factorinformation source, and/or a process factor information source), or thedatabase as it relates to the originally-collected data itself (i.e.,including, but not limited to, raw data corresponding to one of thescreened plurality of samples determined to have the at least one of theplurality of selected metabolites associated therewith, wherein such rawdata may include information, for example, on chromatographic peakshape, integration, primary spectra, and MS2 spectra (whereapplicable)—see, e.g., FIGS. 7B and 7C). From such an informationsource, it may be possible, for example, to examine various sources ofinfluence on one or more of the screened plurality of samples determinedto have the at least one of the plurality of selected metabolitesassociated therewith, so as to determine, for instance,machine/analytical device level outliers that may be responsible for thecharacteristics of the resulting post-analysis data. Even further, insome instances, this “lower level” information source may be used todetermine, for example, the efficacy of a particular analytical devicein providing reliable and consistent data with respect to the analyzedsample. In other instances, the at least one information source maycomprise, for example, a chemical library (i.e., a chemical libraryinformation source) including chemical-specific information related to aparticular selected (identified) metabolite, or in some instances, acompound formed of a combination of selected metabolites, wherein suchchemical-specific information may include molecular weight,fragmentation data, etc. Still another contemplated information sourcemay comprise publicly or otherwise accessible databases (e.g., a publicchemical database) containing expanded information (i.e., publications,research results, etc.) related to a particular selected (identified)metabolite, or in some instances, a compound formed of a combination ofselected metabolites, wherein such accessible databases may include, forinstance, PubMed, Kegg, etc.

As further shown in FIG. 7A, the at least one information source may beaccessed through, for example, a “drop down” menu 740 that may beassociated, for instance, with the displayed visual indication of thecomparison of the at least one plotted two-dimensional data set for eachof the screened plurality of samples, across a plurality of the screenedplurality of samples, that visually indicate the screened plurality ofsamples determined to have the at least one of the plurality of selectedmetabolites associated therewith, and the trend corresponding thereto,across the plurality of the screened plurality of samples 700. Moreparticularly, since the comparison may ostensibly indicate at least oneselected metabolite associated with the analyzed samples, a selectedinformation source may indicate, for example, a corresponding chemicalstructure 710 of the at least one selected metabolite, chemical details720 regarding the at least one selected metabolite, and/or acharacteristic spectrum or characteristic plot 730 of the at least oneselected metabolite. In other instances, the at least one informationsource may be accessed by selecting one of the screened plurality ofsamples from the displayed visual indication of the comparison of the atleast one plotted two-dimensional data set for each of the screenedplurality of samples, across a plurality of the screened plurality ofsamples, as implemented through cooperation, for example, between theprocessor device 130 and the database. The selected one of the screenedplurality of samples may comprise, for instance, an “outlier” which mayhave a different one or more of the selected metabolites associatedtherewith than the one or more metabolites associated with the overallpopulation of samples. In such instances, the two-dimensional data setcorresponding to the selected one of the screened plurality of samplesmay be displayed in place of the overall comparison such that theselected one of the screened plurality of samples may be directlycompared, on a visual or other basis to the at least one selectedmetabolite determined to be present therein (see, e.g., FIGS. 7B and7C). As previously, any further examination of the post-analysis datamay be facilitated by the at least one of an experimental factor, asample factor, and a process factor originally associated with theas-collected data.

FIG. 8 illustrates yet another additional measure related to thedisclosed further verification/confirmation principle for analyzingmetabolomics data. More particularly, in some instances, it may bedesirable to supplement the verification/confirmation of thepost-analysis results by examining, for example, other metabolites orrelated chemicals that may be associated with one or more of theparticular selected metabolites determined to be present in theplurality of samples in the sample population. As such, the “informationsource” aspect previously disclosed may be expanded to further encompassa relational component configured to relate metabolites and relatedchemicals and/or biochemicals according to a particular pathway, asimplemented through cooperation, for example, between the processordevice 130, the user interface 150, and the database. That is, forexample, a “chemical library” may include chemical entries that arerelated according to various factors such as, for instance, anassociation with a particular metabolic process, and such a relationshipmay be accessible by the user in a tabular form (e.g., as shown in FIG.8) or otherwise, during the further verification/confirmation process.In this regard, one skilled in the art will appreciate that the terms“metabolite,” “biological,” and “biochemical” as used in this contextare generally encompassed under the “chemical” terminology otherwisereferred to herein. As such, a method of analyzing metabolomics data, asotherwise disclosed herein may further include a procedure for screeningthe plurality of samples by associating a first metabolite of theplurality of selected metabolites with the at least one plottedtwo-dimensional data set for each of the plurality of samples, whereinthe plurality of metabolites (and thus the first metabolite) areassociated with a chemical pathway. In such an instance, thethree-dimensional data set corresponding to each of a plurality ofsamples can then be analyzed for the presence (or absence) of a secondmetabolite, as implemented through cooperation, for example, between theprocessor device 130, the user interface 150, and the database, whereinthe second metabolite is related to the first metabolite by the chemicalpathway, so as to verify or otherwise confirm the association of thefirst metabolite with the at least one plotted two-dimensional data setfor each of the plurality of samples.

In a similar aspect, once a selected metabolite has been determined tobe present in a particular sample or sample population, it may sometimesbe beneficial for the user to be able to examine other instances inwhich that selected metabolite has been identified, as a cross-referenceor further consistency check with a particular analysis. As such, asshown schematically in FIG. 9, the “information source” aspect may befurther expanded to encompass another relational component (i.e.,database or archive) configured to relate a particular selectedmetabolite identified as being present in a particular sample or in thesample population, to completed studies (whether related or not, orsimilar or not) where the same selected metabolite may also have beenidentified as being present in the analyzed sample or sample population,as implemented through cooperation, for example, between the processordevice 130, the user interface 150, and the database. That is, forexample, at least one study factor of the three-dimensional data setcorresponding to each of a plurality of samples and having the at leastone of the plurality of selected metabolites associated therewith, asdetermined for a particular study, may be compared with a correspondingat least one study factor of another three-dimensional data setcorresponding to each of a plurality of samples and having the at leastone of the plurality of selected metabolites associated therewith, asdetermined for a previous study, so as to determine an inter-studyrelation therebetween. In this manner, various study factors such as,for example, the context of the study, the analyzed sample matrices, theeffect or manifestation of the particular selected metabolite withrespect to a certain disease state, etc. can be compared between studiesas a cross reference or further consistency check with respect to theanalyzed data.

In a further aspect, once a selected metabolite has been determined tobe present in a particular sample or sample population, it may sometimesbe beneficial for the user to be able to visually examine the effect ofvarious sample-related factors on the post-analysis results. As such, asshown schematically in FIG. 10, the processor device 130 may be furtherconfigured to cooperate with the user interface 150, and the database soas to allow an axis parameter of the comparison (i.e., thesample-related factor plotted with respect to the x-axis) of the atleast one plotted two-dimensional data set for each of the screenedplurality of samples, across a plurality of the screened plurality ofsamples, to be varied so as to visually indicate to the user an effectof the particular sample-related factor associated with that axisparameter, across the plurality of the screened plurality of samples.For example, such variation of the x-axis parameter of the comparisonmay be implemented, for example, between the processor device 130, theuser interface 150, and the database, by changing the x-axis parameterto or between such sample-related parameters as, for instance, aretention index, a retention time, a peak area, and a metabolite mass.Accordingly, such a capability further facilitates an evaluation of theconfidence or certainty of the post-analysis results regardingidentification of the one or more selected metabolites, and allows othersample-related factors to be additionally analyzed to supplement crossreference or further consistency information with respect to theanalyzed data.

In addition to providing appropriate apparatuses and methods,embodiments of the present invention may also provide associatedcomputer program products for performing the functions/operationsdisclosed above. Such computer program products may include a computerreadable storage medium having appropriate computer readable programcode embodied in and stored by the medium, and executable or otherwiseaccessible by an appropriate computer device. With reference to FIG. 3,the computer readable storage medium may be, for example, part of thememory device 140, and may implement the computer readable program codeto perform the above discussed operations.

In this regard, FIG. 3 is a block diagram illustration of methods,systems and computer program products according to embodiments of theinvention. It will be understood that each block or step of the blockdiagram and combinations of blocks in the block diagram can beimplemented by appropriate computer program instructions. These computerprogram instructions may be loaded onto a computer device or otherprogrammable apparatus for executing the functions specified in theblock diagram, flowchart or control flow block(s) or step(s), otherwiseassociated with the method(s) disclosed herein. These computer programinstructions may also be stored in a computer-readable memory, so as tobe accessible by a computer device or other programmable apparatus tofunction in a particular manner, such that the instructions stored inthe computer-readable memory produce an article of manufacture capableof directing or otherwise executing instructions which implement thefunctions specified in the block diagram, flowchart or control flowblock(s) or step(s), otherwise associated with the method(s) disclosedherein. The computer program instructions may also be loaded onto acomputer device or other programmable apparatus to cause a series ofoperational steps to be performed on the computer device or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions executed by the computer device or otherprogrammable apparatus provide or otherwise direct appropriate steps forimplementing the functions specified in the block diagram, flowchart orcontrol flow block(s) or step(s), otherwise associated with themethod(s) disclosed herein.

Accordingly, blocks or steps of the block diagram, flowchart or controlflow illustrations support combinations for performing the specifiedfunctions, combinations of steps for performing the specified functions,and/or program instruction for performing the specified functions. Itwill also be understood that each block or step of the block diagram,flowchart or control flow illustrations, and combinations of blocks orsteps in the block diagram, flowchart or control flow illustrations, canbe implemented by special purpose hardware-based computer systems whichperform the specified functions or steps, or combinations of specialpurpose hardware and computer instructions (software).

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

1. A method of analyzing metabolomics data, comprising: automaticallyreceiving, in a database, a three-dimensional data set of metabolomicsdata corresponding to each of a plurality of samples, from each of afirst data source and a second data source; for the metabolomics datafrom each of the first and second data sources: converting thethree-dimensional data set into at least one correspondingtwo-dimensional data set for each of the plurality of samples; plottingthe at least one two-dimensional data set for each of the plurality ofsamples; comparing the at least one plotted two-dimensional data set foreach of the plurality of samples to a plurality of characteristic plotscorresponding to a plurality of selected metabolites so as to screen theplurality of samples by associating at least one of the plurality ofselected metabolites with the at least one plotted two-dimensional dataset for each of the plurality of samples; and comparing, for thescreened plurality of samples determined to have the at least one of theplurality of selected metabolites associated therewith, the at least oneplotted two-dimensional data set for each of the screened plurality ofsamples, across the screened plurality of samples, so as to determine atrend of the associated at least one of the plurality of selectedmetabolites, across the screened plurality of samples; displaying avisual indication of the comparison of the at least one plottedtwo-dimensional data set for each of the screened plurality of samples,across a plurality of the screened plurality of samples, to visuallyindicate the screened plurality of samples determined to have the atleast one of the plurality of selected metabolites associated therewith,and the trend corresponding thereto, across the plurality of thescreened plurality of samples, for the metabolomics data from each ofthe first and second data sources; and comparing the first and seconddata sources with respect to the at least one of the plurality ofselected metabolites determined to be included in the metabolomics dataand the trend across the screened plurality of samples associatedtherewith so as to determine an identity confidence measure associatedwith the at least one of the plurality of selected metabolites.
 2. Amethod according to claim 1, wherein the automatically receiving stepfurther comprises: compiling the three-dimensional data sets from theplurality of samples into a population data set; and indexing thethree-dimensional data sets by sample.
 3. A method according to claim 1,wherein the visual indication comprises a plot of corresponding ones ofthe at least one plotted two-dimensional data set for each of theplurality of the screened plurality of samples versus a characteristictime at which a characteristic intensity peak of the at least one of theplurality of selected metabolites is expected.
 4. A method according toclaim 3, further comprising retrieving the at least one plottedtwo-dimensional data set corresponding to each of a subset of sampleslacking the characteristic intensity peak of the at least one of theplurality of selected metabolites at the characteristic time; anddisplaying the at least one plotted two-dimensional data setcorresponding to each of the subset of samples for subjectiveevaluation.
 5. A method according to claim 3, wherein the step ofcomparing the at least one plotted two-dimensional data set for each ofthe screened plurality of samples, further comprises comparing acharacteristic time at which a characteristic intensity peak associatedwith the at least one of the plurality of selected metabolites isexpected in the at least one plotted two-dimensional data set for eachof the screened plurality of samples, across the screened plurality ofsamples, so as to determine a trend of the associated at least one ofthe plurality of selected metabolites, across the screened plurality ofsamples.
 6. A method according to claim 1, wherein the automaticallyreceiving step further comprises automatically receiving athree-dimensional data set of metabolomics data corresponding to each ofa plurality of samples from each of a first data source and a seconddata source, each of the first and second data sources comprising achromatograph.
 7. A method according to claim 1, wherein theautomatically receiving step further comprises automatically receiving athree-dimensional data set of metabolomics data corresponding to each ofa plurality of samples from each of a first data source and a seconddata source, each of the first and second data sources comprising one ofa gas chromatograph, a positive channel of a liquid chromatograph, and anegative channel of a liquid chromatograph.
 8. A system for analyzingmetabolomics data, comprising: a database in communication with each ofa first data source and a second data source, the database beingconfigured to automatically receive a three-dimensional data set ofmetabolomics data corresponding to each of a plurality of samples, fromeach of the first and second data sources; a processor device incommunication with said database, said processor device beingconfigured, for the metabolomics data from each of the first and seconddata sources, to: convert the three-dimensional data set into at leastone corresponding two-dimensional data set for each of the plurality ofsamples; plot the at least one two-dimensional data set for each of theplurality of samples; compare the at least one plotted two-dimensionaldata set for each of the plurality of samples to a plurality ofcharacteristic plots corresponding to a plurality of selectedmetabolites so as to screen the plurality of samples by associating atleast one of the plurality of selected metabolites with the at least oneplotted two-dimensional data set for each of the plurality of samples;and compare, for the screened plurality of samples determined to havethe at least one of the plurality of selected metabolites associatedtherewith, the at least one plotted two-dimensional data set for each ofthe screened plurality of samples, across the screened plurality ofsamples, so as to determine a trend of the associated at least one ofthe plurality of selected metabolites, across the screened plurality ofsamples; and a user interface in communication with said database andsaid processor device, the user interface being configured to display avisual indication of the comparison of the at least one plottedtwo-dimensional data set for each of the screened plurality of samples,across a plurality of the screened plurality of samples, to visuallyindicate the screened plurality of samples determined to have the atleast one of the plurality of selected metabolites associated therewith,and the trend corresponding thereto, across the plurality of thescreened plurality of samples, for the metabolomics data from each ofthe first and second data sources, the displayed visual indication beingfurther configured to facilitate determination of an identity confidencemeasure associated with the at least one of the plurality of selectedmetabolites from a comparison of the first and second data sources withrespect to the at least one of the plurality of selected metabolitesdetermined to be included in the metabolomics data and the trend acrossthe screened plurality of samples associated therewith.
 9. A systemaccording to claim 8, further comprising a memory device incommunication with said database and said processing device, the memorydevice being configured to store the plurality of characteristic plots.10. A system according to claim 8, wherein each of the first and seconddata sources is selected from the group consisting of: a nuclearmagnetic resonance imaging device; a spectrometry device; anelectrochemical array device; a chromatograph; and combinations thereof.11. A system according to claim 8, wherein each of the first and seconddata sources comprises one of a gas chromatograph, a positive channel ofa liquid chromatograph, and a negative channel of a liquidchromatograph.
 12. A system according to claim 8, wherein the databaseis further configured to compile the three-dimensional data sets fromthe plurality of samples into a population data set, and to index thethree-dimensional data sets by sample, upon automatically receiving thethree-dimensional data sets from each of the first and second datasources.
 13. A system according to claim 8, wherein the visualindication displayed by the user interface further comprises a plot ofcorresponding ones of the at least one plotted two-dimensional data setfor each of the plurality of the screened plurality of samples versus acharacteristic time at which a characteristic intensity peak of the atleast one of the plurality of selected metabolites is expected.
 14. Asystem according to claim 13, wherein the processing device is furtherconfigured to retrieve the at least one plotted two-dimensional data setcorresponding to each of a subset of samples lacking the characteristicintensity peak of the at least one of the plurality of selectedmetabolites at the characteristic time, and to displaying the at leastone plotted two-dimensional data set corresponding to each of the subsetof samples on the user interface for subjective evaluation.
 15. A systemaccording to claim 13, wherein the processing device is furtherconfigured to compare a characteristic time at which a characteristicintensity peak associated with the at least one of the plurality ofselected metabolites is expected in the at least one plottedtwo-dimensional data set for each of the screened plurality of samples,across the screened plurality of samples, so as to determine a trend ofthe associated at least one of the plurality of selected metabolites,across the screened plurality of samples.